• The Journal of Churna Manual Medicine for Spine and Nerves
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• v.10 no.1
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• pp.47-61
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• 2015

Objectives : The purpose of this study is body parameters characteristics through posture analysis system of musculoskeletal problem patient Methods : Posture analysis system were performed for 164 patients to measure body parameters such as Q-angle, body inclination, neck inclination, PCMT(posterior cervical muscle tension), Knee flexion and posture balance. Statistical analysis using statistical analysis techniques and Pearson correlation coefficients was performed to assess the body parameters obtained by posture analysis system. Results : More than half of people out of 164 reported low back pain, 34.8% of the total was found to have neck pain. There was not a significant difference between genders from the characteristics of gender based body parameters expect for the statistical difference in Q angle, PCMT. There was a significant correlation between low back pain and multiple response status. There was a significant correlations between knee pain and Q angle. Also There was a significant correlations between pelvic pain and posture balance of ankle. Conclusions : Posture analysis system can be used to perform the analysis in place of X-ray measuring body posture and clinical parameters. The results of this study are expected to be the basis for further research on the clinical application of posture analysis system.

• Journal of Korean Medicine Rehabilitation
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• v.24 no.4
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• pp.177-185
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• 2014

Objectives This study analyzed the correlation between body parameters measured using X-ray and Chuna posture analysis system to determine their clinical value in diagnosing and evaluating skeletomuscular diseases. Methods X-ray and Chuna posture analysis system were performed for 105 patients to measure physical characteristics such as Interacromial angle, Pelvic obliquity angle, Structural leg length discrepancy (SLLD), Midpatella-midtalus angle (MMA) and Q-angle, Anterior head translation (AHT), Anterior superior iliac spine to posterior superior iliac spine angle (ASIS-PSIS angle), Interscapular angle, Scoliotic angle and Cobb's angle. Statistical analysis using statistical analysis techniques and Pearson correlation coefficients was performed to assess the body parameters obtained by X-ray and Chuna posture analysis system. Results Significant correlations were observed between the values for Interacromial angle, Pelvic obliquity angle, SLLD, MMA and Q-angle, AHT, ASIS-PSIS angle, Interscapular angle, Scoliotic angle and Cobb's angle obtained by X-ray and Chuna posture analysis system. Significant correlations were also observed between right MMA and left Q-angle as well as between left MMA and right Q-angle. Conclusions Chuna posture analysis system can be used instead of X-ray measure body parameters and perform posture analysis in clinical practice. This study's findings are expected to serve as a basis for further research on the clinical application of Chuna posture analysis system.

• Journal of the Ergonomics Society of Korea
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• v.14 no.1
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• pp.69-81
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• 1995

Few studies on the biomechanical analysis of hand postures and tool handling tasks exist because of the lack of appropriate measurement techniques for hand force. A measurement system for the finger forces and joint angles for the analysis of manual tool handling tasks was developed in this study. The measurement system consists of a force sensing glove made from twelve Force Sensitive Resistors and an angle-measuring glove (Cyberglove$^{TM}$, Virtual technologies) with eighteem joint angle sensors. A biomechanical model of the hand using the data from the measurement system was also developed. Systems of computerized procedures were implemented inte- grating the hand posture measurement system, biomechanical analysis system, and the task analysis system for manual tool handling tasks. The measurement system was useful in providing the hand force data needed for an existing task analysis system used in CTD risk evaluation. It is expected that the hand posture measurement developed in this study will provide an efficient and cost-effective solution to task analysis of manual tool handling tasks.s.

• Korean Journal of Applied Biomechanics
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• v.14 no.3
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• pp.49-65
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• 2004

The purpose of this study is development of posture evaluation and Range of Motion(ROM) system by using digital vision analysis method. The results of this study are as follows. First, Scoliosis evaluation through this research measurement system represent 3mm error in 7 cervical point and deepest lumbar point, 0.7mm error in other point. This mean this research measurement system have a reliability for scoliosis evaluation. Second, for spine line evaluation on high fat subject, we need reconstrection spine line after measurement for fat thickness in 7 cervical point and deepest lumbar point. Third, In pedioscope error test, it present 0.01848cm in X axis and 0.01757cm in Y axis. This results mean pedioscope have a reliability foot evaluation. Forth, Posture evaluation and Range of Motion measurement system by using digital vision analysis method can fast measure in range of motion and foot evaluation and posture. therefore we can expect this system application in young people posture clinic center and hospital and so on.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.5
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• pp.151-157
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• 2011

This study analyzed the posture balance of time variation for exercising body a period of time. Posture balance measured output values for the posture balance system of body moving in the multi-parameter. Posture moving variation had three methods such as open and closed eye, head moving and upper body moving. There were checked a parameter that measured vision, vestibular, somatosensory, CNS. This system was evaluated a data through the stability. This system has catched a signal for physical condition of body data such as a data acquisition system, data signal processing and feedback system. The output signal was generated Fourier analysis that using frequency of 0.1Hz, 0.1-0.5Hz, 0.5-1Hz and 1Hz over. The posture balance system will be used to support assessment for body moving the posture balance of time variation. It was expected to monitor a physical parameter for health verification system.

• Journal of the Ergonomics Society of Korea
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• v.24 no.3
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• pp.43-52
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• 2005

A two-dimensional posture measurement system was developed to evaluate the risks of work-related musculoskeletal disorders(MSDs) easily on various conditions of work. The posture measurement system is an essential tool to analyze the workload for preventing work-related musculoskeletal disorders. Although several posture measurement systems have been developed for workload assessment, some restrictions in industry still exist because of its difficulty on measuring work postures. In this study, an image recognition algorithm was developed based on a neural network method to measure work posture. Each joint angle of human body was automatically measured from the recognized images through the algorithm, and the measurement system makes it possible to evaluate the risks of work-related musculoskeletal disorders easily on various working conditions. The validation test on upper body postures was carried out to examine the accuracy of the measured joint angle data from the system, and the results showed good measuring performance for each joint angle. The differences between the joint angles measured directly and the angles measured by posture measurement software were not statistically significant. It is expected that the result help to properly estimate physical workload and can be used as a postural analysis system to evaluate the risk of work-related musculoskeletal disorders in industry.

• Korean Journal of Agricultural Science
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• v.42 no.2
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• pp.117-124
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• 2015

In forest work, working conditions are very hard to improve. The good posture is believed to bring about direct improvements such as accident prevention. Therefore, this research carried on analysis of working posture in forest work (construct in stepping-stone) using OWAS analysis system. According to the analytical results provided by OWAS, the ratio of category III (Work posture has a distinctly harmful effect on the musculoskeletal system) has shawn that worker 2 was 32.2%, worker 1 was 25.2% and worker 3 was 15.5%. Furthermore, the ratio of category IV (Work posture with an extremely harmful effect on the musculoskeletal system) has shown that worker 2 was 9.8%, worker 3 was 1.4% and worker 1 was 1.2%. According to the OWAS method, percentage of OWAS action categories III and IV in the worker 2 was higher than another workers.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.6
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• pp.117-125
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• 2015

In the modern age, it is important for people to maintain a good sitting posture because they spend long hours sitting. Posture correction treatment requires a great deal of time and expenses with continuous observation by a specialist. Therefore, there is a need for a system with which users can judge and correct their postures on their own. In this study, we collected users' postures and judged whether they are normal or abnormal. To obtain a user's posture, we propose a four-joint motion capture system that uses inertial sensors. The system collects the subject's postures, and features are extracted from the collected data to build a database. The data in the DB are classified into normal and abnormal postures after posture learning using the K-means clustering algorithm. An experiment was performed to classify the posture from the joints' rotation angles and positions; the normal posture judgment reached a success rate of 99.79%. This result suggests that the features of the four joints can be used to judge and help correct a user's posture through application to a spinal disease prevention system in the future.

• The Journal of Korean Academy of Orthopedic Manual Physical Therapy
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• v.19 no.2
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• pp.1-9
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• 2013

Background: The purpose of this study is to analysis of correlation upper body according to forward head posture. Methods: The subjects of this study were 40 female university students who were equally and randomly allocated to a forward head posture group, normal group. Using general posture system, electromyograph, visual analogue scale, tape measurement, neck disability index were evaluated. Results: There was positive correlation between posture analysis and Sternocleidomastoid, neck flexion (p<.05). There was positive correlation between Craniovertebral angle (CVA) and trapezius upper, VAS (p<.05). There was negative correlation between posture analysis and CVA (p<.05). There was negative correlation between Cranial rotation angle and CVA (p<.05). Conclusion: Increased forward head posture lead to increase of pain, muscles activity, so it suggests to be necessary on the prevention of dysfunction and limited activities daily living.

• Journal of information and communication convergence engineering
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• v.17 no.1
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• pp.74-83
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• 2019

This study designs a squat posture recognition system that can provide correct squat posture guidelines. This system comprises two modules: a Kinect camera for monitoring users' body movements and a Wii Balance Board(WBB) for measuring balanced postures with legs. Squat posture recognition involves two states: "Stand" and "Squat." Further, each state is divided into two postures: correct and incorrect. The incorrect postures of the Stand and Squat states were classified into three and two different types of postures, respectively. The factors that determine whether a posture is incorrect or correct include the difference between shoulder width and ankle width, knee angle, and coordinate of center of pressure(CoP). An expert and 10 participants participated in experiments, and the three factors used to determine the posture were measured using both Kinect and WBB. The acquired data from each device show that the expert's posture is more stable than that of the subjects. This data was classified using a support vector machine (SVM) and $na{\ddot{i}}ve$ Bayes classifier. The classification results showed that the accuracy achieved using the SVM and $na{\ddot{i}}ve$ Bayes classifier was 95.61% and 81.82%, respectively. Therefore, the developed system that used Kinect and WBB could classify correct and incorrect postures with high accuracy. Unlike in other studies, we obtained the spatial coordinates using Kinect and measured the length of the body. The balance of the body was measured using CoP coordinates obtained from the WBB, and meaningful results were obtained from the measured values. Finally, the developed system can help people analyze the squat posture easily and conveniently anywhere and can help present correct squat posture guidelines. By using this system, users can easily analyze the squat posture in daily life and suggest safe and accurate postures.